Instead of having separate ExchangeCalendar and TradingSchedule objects, we
now just have TradingCalendar. The TradingCalendar keeps track of each
session (defined as a contiguous set of minutes between an open and a close).
It's also responsible for handling the grouping logic of any given minute
to its containing session, or the next/previous session if it's not a market
minute for the given calendar.
This commit removes the ability to reference a shared TradingEnvironment through the zipline.finance.trading module. In place, the classes that require a TradingEnvironment, or its child AssetFinder, contain their own references to those objects.
This commit also adds serialization utilities that allow for the pickling/unpickling of objects without unintentionally their TradingEnvironments or AssetFinders.
Instead of using the pandas.Series datetime index for every single
vector, get the index at the beginning of the update loop based on the
dt and then use that index to set the values.
Also, since the dt lookup is no longer needed, store the values as numpy
arrays, which are more lightweight.
Locally, this patch cuts out about 60% of the time spent in the update
method.
The calculations that are expected to change are:
- cumulative.beta
- cumulative.alpha
- cumulative.information
- cumulative.sharpe
- period.sortino
* Explanation of how risk calculations are changing
** Risk Fixes for Both Period and Cumulative
*** Downside Risk
Use sample instead of population for standard deviation.
Add a rounding factor, so that if the two values are close for a given
dt, that they do not count as a downside value, which would throw off
the denominator of the standard deviation of the downside diffs.
*** Standard Deviation Type
Across the board the standard deviation has been standardized to using
a 'sample' calculation, whereas before cumulative risk was monstly using
'population'. Using `ddof=1` with `np.std` calculates as if the values
are a sample.
** Cumulative Risk Fixes
*** Beta
Use the daily algorithm returns and benchmarks instead of annualized
mean returns.
*** Volatility
Use sample instead of population with standard deviation.
The volatility is an input to other calculations so this change affects
Sharpe and Information ratio calculations.
*** Information Ratio
The benchmark returns input is changed from annualized benchmark returns
to the annualized mean returns.
*** Alpha
The benchmark returns input is changed from annualized benchmark returns
to the annualized mean returns.
** Period Risk Fixes
*** Sortino
Use the downside risk of the daily return vs. the mean algorithm returns
for the minimum acceptable return instead of the treasury return.
The above required adding the calculation of the mean algorithm returns
for period risk.
Also, use algorithm_period_returns and tresaury_period_return as the
cumulative Sortino does, instead of using algorithm returns for both
inputs into the Sortino calculation.
* Other Supporting Changes
** answer_key
Add new mappings for downside risk and Sortino as well as
re-address the index mappings because of changes to the answer key
spread sheet.
** test_risk_cumulative
Change the decimal precision to expect higher precision.
The calculations are now more aligned with the answer key, so we can
expect higher precision. In particular now that the standard deviation
type matches everywhere in both the Python implementation and the answer
sheet, the precision of the first value no longer has to be glossed over.
** test_events_through_risk
Change the results which are used as a canary for risk changes,
since we do expect Sharpe to change with this change..
The risk unit tests were using the public Yahoo! data instead
of the returns from the answer key spreadsheet, change the RiskPeriod's
created in tests to use the values in the benchmark returns
column of the answer key.
Also, change the spreadsheet's benchmark volatility calculation
to use sample.
The use of population was exposed when the input values were
corrected.
Remove the lists of DailyReturn objects in favor of using pd.Series
to store the return values.
Should make it easier to inspect the values when stepping through,
make the windowing of data to a certain range more facile by using,
and have some performance increases due to removing object creation
and member access.
Instead of using the indexes defined in the answer key class
to index back into the answer key object, populate the answers
so that they are available as members of the answer key object.
Update period risk test to use new answer key structure.
Also, remove the rounding behavior from the answer sheet, leaving
the rounding to the consumer of the answer key values, so that
the values can be retrieved from the spreadsheet during answer
key __init__ without knowledge of the decimal point that the calling
code expects.
Correspondingly, change period risk tests to use
np.testing.assert_almost_equal when doing floating point comparison.
Conner Fromknecht